Image Compression Using Deep Learning Techniques

This project is a proof of concept that it is possible to train large and deep convolutional neural networks (CNN) for JPEG2000 compression artifacts reduction, and that such networks can provide significantly better reconstruction quality compared to JPEG2000 decoding and image reconstruction method.

We were able to train deep networks in relatively short time by using residual learning architecture and provide further insights into convolutional networks for JPEG2000 artifact reduction by evaluating according to common criteria with respect to JPEG2000 quality level and original image.